{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Eaton method with well log\n",
    "\n",
    "Pore pressure prediction with Eaton's method using well log data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Steps:\n",
    "\n",
    "1. Calculate Velocity Normal Compaction Trend\n",
    "\n",
    "2. Optimize for Eaton's exponent n\n",
    "\n",
    "3. Predict pore pressure using Eaton's method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {"nbsphinx": "hidden"},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')\n",
    "\n",
    "# for python 2 and 3 compatibility\n",
    "# from builtins import str\n",
    "# try:\n",
    "#     from pathlib import Path\n",
    "# except:\n",
    "#     from pathlib2 import Path\n",
    "#--------------------------------------------\n",
    "import sys\n",
    "ppath = \"../..\"\n",
    "\n",
    "if ppath not in sys.path:\n",
    "    sys.path.append(ppath)\n",
    "#--------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function, division, unicode_literals\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use(['seaborn-paper', 'seaborn-whitegrid'])\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import pygeopressure as ppp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Calculate Velocity Normal Compaction Trend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create survey with the example survey `CUG`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set to the directory on your computer\n",
    "SURVEY_FOLDER = \"C:/Users/yuhao/Desktop/CUG_depth\"\n",
    "\n",
    "survey = ppp.Survey(SURVEY_FOLDER)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve well `CUG1`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "well_cug1 = survey.wells['CUG1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get velocity log:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "vel_log = well_cug1.get_log(\"Velocity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View velocity log:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xed9e860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_vel, ax_vel = plt.subplots()\n",
    "ax_vel.invert_yaxis()\n",
    "vel_log.plot(ax_vel)\n",
    "well_cug1.plot_horizons(ax_vel)\n",
    "\n",
    "# set fig style\n",
    "ax_vel.set(ylim=(5000,0), aspect=(5000/4600)*2)\n",
    "ax_vel.set_aspect(2)\n",
    "fig_vel.set_figheight(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimize for NCT coefficients a, b:\n",
    "\n",
    "`well.params['horizon']['T20']` returns the depth of horizon T20."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "a, b = ppp.optimize_nct(\n",
    "    vel_log=well_cug1.get_log(\"Velocity\"),\n",
    "    fit_start=well_cug1.params['horizon'][\"T16\"],\n",
    "    fit_stop=well_cug1.params['horizon'][\"T20\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And use a, b to calculate normal velocity trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pygeopressure.velocity.extrapolate import normal_log\n",
    "nct_log = normal_log(vel_log, a=a, b=b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View fitted NCT:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf6db550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_vel, ax_vel = plt.subplots()\n",
    "ax_vel.invert_yaxis()\n",
    "# plot velocity\n",
    "vel_log.plot(ax_vel, label='Velocity')\n",
    "# plot horizon\n",
    "well_cug1.plot_horizons(ax_vel)\n",
    "# plot fitted nct\n",
    "nct_log.plot(ax_vel, color='r', zorder=2, label='NCT')\n",
    "\n",
    "# set fig style\n",
    "ax_vel.set(ylim=(5000,0), aspect=(5000/4600)*2)\n",
    "ax_vel.set_aspect(2)\n",
    "ax_vel.legend()\n",
    "fig_vel.set_figheight(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save fitted nct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# well_cug1.params['nct'] = {\"a\": a, \"b\": b}\n",
    "\n",
    "# well_cug1.save_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Optimize for Eaton's exponent n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to preprocess velocity.\n",
    "\n",
    "Velocity log processing (filtering and smoothing):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "vel_log_filter = ppp.upscale_log(vel_log, freq=20)\n",
    "\n",
    "vel_log_filter_smooth = ppp.smooth_log(vel_log_filter, window=1501)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Veiw processed velocity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf6dbf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_vel, ax_vel = plt.subplots()\n",
    "ax_vel.invert_yaxis()\n",
    "# plot velocity\n",
    "vel_log.plot(ax_vel, label='Velocity')\n",
    "# plot horizon\n",
    "well_cug1.plot_horizons(ax_vel)\n",
    "# plot processed velocity\n",
    "vel_log_filter_smooth.plot(ax_vel, color='g', zorder=2, label='Processed', linewidth=1)\n",
    "\n",
    "# set fig style\n",
    "ax_vel.set(ylim=(5000,0), aspect=(5000/4600)*2)\n",
    "ax_vel.set_aspect(2)\n",
    "ax_vel.legend()\n",
    "fig_vel.set_figheight(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the processed velocity data for pressure prediction."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimize Eaton's exponential `n`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = ppp.optimize_eaton(\n",
    "    well=well_cug1, \n",
    "    vel_log=vel_log_filter_smooth, \n",
    "    obp_log=\"Overburden_Pressure\", \n",
    "    a=a, b=b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the RMS error variation with `n`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121711d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pygeopressure.basic.plots import plot_eaton_error\n",
    "\n",
    "fig_err, ax_err = plt.subplots()\n",
    "\n",
    "plot_eaton_error(\n",
    "    ax=ax_err,\n",
    "    well=well_cug1, \n",
    "    vel_log=vel_log_filter_smooth, \n",
    "    obp_log=\"Overburden_Pressure\", \n",
    "    a=a, b=b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save optimized n:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# well_cug1.params['nct'] = {\"a\": a, \"b\": b}\n",
    "\n",
    "# well_cug1.save_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.Predict pore pressure using Eaton's method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate pore pressure using Eaton's method requires velocity, Eaton's exponential, normal velocity, hydrostatic pressure and overburden pressure.\n",
    "\n",
    "`Well.eaton()` will try to read saved data, users only need to specify them when they are different from the saved ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pres_eaton_log = well_cug1.eaton(vel_log_filter_smooth, n=n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View predicted pressure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EIgfMSzl16vKjrtta3sa3W77VKZUQnsktSsUSZSHDepx5K7TLvh4bFsu6I+vkRkMhnMgtSqVrRFdMiolftiy96rkbm97Igp0LdEglhGdyi1IJ8g0iNiyWw1k7r3quR8MesvCYEE7kFqUCsGzkMq4reOWqryuKQpcGXVi2f5kOqYTwPG5TKr5evtSrB5cMVrxoYPRA5qTOcX4oITyQ25QKQPfusGTJ1V83KSbahrZl7ZG1zg8lhIdxq1JRVdi+vfjnbm95O99s/sa5gYTwQG5VKqUtDudt9qZJrSaknkh1XiAhPJBblUpeQR5HfJI4dOZksc8PbzOcLzZ84eRUQngWtyqVM9ln+KTQwmfLih+X4uflR1hgGHvO7HFyMiE8h1uVyjXVr6FxUDR/bL96ENwF97S7h0/XferEVEJ4FrcqFYCeTSxsTi+5VAJ8Agj0DSx2IW8hRNW5XalYohI5rezkcPrhEl9zf+z9fLT2IyemEsJzuGGpWABYurfko5Wa1WpiUkycyjzlrFhCeAy3K5XwwHBirfdTu1rdUl8nRytCOIbhJ2kqztiID2lWxu8sJCCEzLxMzuWcI8g3yDnBhPAAbnekAhAdDVu3lv26+2Lv45O1nzg+kBAexG1LJbUcA2cjgiM4kXmCrLwsx4cSwkO4ZanUqAFnz5bvtSPajuDz9Z87NI8QnsQtSwWgvDNINqvdjD1n95BbkOvYQEJ4CLctlfzgHQz9biSns06X+do7W9/JVxu/ckIqIdyf25ZK2zYKX6d+Xq4Z31qHtGbL8S3kFeQ5IZkQ7s1tS6V/t8YEEl7qILhLDWs9jNmbZjs4lRDuz21LpVo1hQYFFpbuK1+pxIbFsvHYRvIL8x2cTAj35ralAtAhxMK6I+s4l3OuXK+/veXtfLtZFh8ToirculQeuCGRQgpZvn95uV6fUC+BtUfWUlBY4OBkQrgvty6VDo2ac0vOXDo36Fzu9wyOGcx/t/7XgamEcG9uXSqKotC3WT8O7wku93s6NejEqkOrKLQWOjCZEO7LrUsFYMAAmDu3Yu+5JfoWWSdIiEpy+1KpUQMyMyv2nq4RXVm2f5kcrQhRCW5fKpXVv3l/ftJ+0juGEIbjEaViNkN2bsVGy1oiLfy590+s5b2JSAgBeEipHKn9Lde8WqdCUxwoikKfZn2Yv2O+A5MJ4X48olTiGzUlPe8cyYeSK/S+Hg17sHD3QjlaEaICPKJUbmjTBj8lqNxD9i9QFIXeTXrz+67fHZRMCPfjEaUSHmamfmHXCpcKQO/Gvfl1569ytCJEOXlEqZhMEFFoYeWBlRWejElRFHo06sHiPYsdlE4I9+IRpQIQYU3Ez8uPXad3Vfi9fZr24eftPzsglRDux2NKJSa4PTsfOEl03egKv1dRFCxRtkvMQojSeUypNFdN7N5V+d9uf7U/c7fNlXMrQpTBY0qlTRtYtary71cUhWujrmXJ3iX2CyWEG/KYUomIgH37yj/LfnH6qf2Yp82ToxUhSuExpQIQHw8pKZV/v6Io9GzUk0V7FtkvlBBuxqNKpX9/+HLe/ioNZuvTtA+/bP9FjlaEKIFHlYqvL6zjU4Z8P6TSU0YqikKvxr34Y/cfdk4nhHvwqFIBuCUukbScNDYd31TpbdzY5Ebm75gvRytCFMPjSuW+GzpisnqXez2g4iiKwo1NbuTXnb/aMZkQ7sHjSqW6jz/1aF+p+4AudX3j6/l91+9ytCLEFbwctWFVVe8CagDfAjOBc8D9gF9pjzVNK98iPZV07BhE+1tI2vcBhdZCTErlelVRFG5qehPzd8ynT7M+dk4phHE5pFRUVW0NWICNwBNFP3yB4UBkGY/fcUSmC06cgMSQvrQIzSIrL4vqPtUrva2ejXryyG+PcFPTm1AUxY4phTAuh3z80TRtI/AFoAChmqZtBjYBDcvx2KF8fSHKqyOv9369SoUCtqOVfmo/ftouc9kKcYHDPv5gKxSA7KKfzUBAGY9L/Veemppa5VDHjnmxY0cAqalnq7wtgFBrKNM2TKNJQZPLjlays7PtkteZjJbZaHnBmJkrypGlcsFmVVXjsH282Q9klPL4QGkbio6u+B3GVwoPt42qjY4Oq/K2LhjhP4Lt2dsZ0HzAxa+lpqbaJa8zGS2z0fKCMTOnVHAYuqNLxQp8BryL7ajkASAHmFHKY4cKCoIzZ+y7zWujruWRXx+hn9qv0id+hWfw99nH+VNbKCw8j5dPPfyCupN5eg5+wddh9qpFYcF5ss4uQDH54hvQGbN3bb0jV5jDSkXTtKXAheu2d13xdFmPHUZRbD+ys8HPz37bHRg9kDmpcxgUM8h+GxVuJzM3Et+AAPJz9uIX1I3s9GVY+WfRupyMFfgFdcPkVZustEX41+itY9rK8cj/VocMga+/hvfXvM+LSS/aZZuJkYmyqqGoML/ArpjMQRcfFxZkYPYOQVG8UBRnnJ2wP48slVatYPNm2HZS4/2U9+02gG1wzGD+t/V/dtmW8EyXFom1MLuUV7oujywVgJtugoCTFg6eO8ies3vsss0uEV1YeXClHK2ISjN51SU/9xDWwhysVO6mV715bKlcdx2cXt8NoEr3AV3p1phb+X7L93bbnvAsvtXbkpu5gcyzC/AL6KB3nEox1Ie27PQVdt1eh5g6/Ha6CYt3fs8dTVS7bLNdDfhmw480j4iwe15HKyzwNlRmo+UFW+b83DOgmC9mN5mDyMvaxoXVvk3mGmCGvOxd5GVXfPUH+/Ot0KsNVSp+gZ3tur1hw+GtZ3uz3Hu+Xbc9PNaXBRv+oE3rJ+22TWcwmVPxCzTOGAqj5QVjZoaKjVPx2I8/AF5e0Nk0gTlD7DvvbFx4HNpZrcILlwnhDjy6VABCvJvQom5Lu98QOLDhQD5f/7ldtymEEXh8qRQUgNls/+02DW7KrtO7yMrLsv/GhXBhHl8qYWFwoNQ7jirv/rj7+WTdJ47ZuBAuyuNL5aabYO5cx2y7Sa0mHEk/QkZuhmN2IIQL8vhSadDAdqRSWGh1yEeVUfGj+GDNB3bfrhCuyuNLBaBDB2g3vRuP//643bcdERxBWk4aZ7PtM3+LEK5OSgXo2xc43azKk2GXZHT8aN5b/Z5Dti2Eq5FSAXx8oJHJwpYTWziZedLu2w8LDCO/MJ8T50/YfdtCuBoplSIP3mAB4K99fzlk+6MTRvP+mvcdsm0hXImUSpFeCZEEWRs47CNQHf86eJu9OZx+2CHbF8JVSKkUMZkUoqw9OJV1ymH7GB0vRyvC/RnqhkJHG1nzU4Z2cdz6PcF+wdTwq8Hes3uJqhHlsP0IoSc5UrlEt24Ky5Y5dh+j4kbxYcqHjt2JEDqSUrlEmzawaZNj91HdpzrhgeFsP7XdsTsSQidSKpfw8oJcJ8xWcF/sfXy89mPH70gIHUipXMHHB9LSHLsPPy8/mtZqyqZjDj4sEkIHUipXGDGygCfe/w3tpObQ/dzd9m5mbpjp0H0IoQcplStENDAxK+tOPlr9uUP342P2oU1IG1YfWu3Q/QjhbFIqV1AUhW6RifwvxTGD4C41tNVQZm+a7fD9COFMUirFuCnGwv781Rw+kenQ/ZhNZhIjE1m8Z7FD9yOEM0mpFMMSaaFQyWfiuysdvq8BzQcwd9tcu068LYSepFSK0SqkFTX8anDCfylbtzp2X4qi0L95f+ZqDpp+Tggnk1IphkkxManrJIZeG8/ChY7f33UNr+PPvX9SUGjMZS6FuJSUSgme6PIEvaP6YXLSd2hYq2HM2jTLOTsTwoGkVEqRn28bZesMCfUS2HhsIzn5Oc7ZoRAOIqVSirp14dgx5+3vvtj7ZEkPYXhSKqVw1r1AFzSv05yD5w6SnpPuvJ0KYWdSKmUICoIzZ5y3vwfjH5SJnIShSamU4sT5EywPu53nP6nYqvdVEREcQWZeJqcyHTcDnRCOJKVSimC/YBYd/ImN6Qs54cSJ8MckjGHG6hnO26EQdiSlUgofsw+d6nfC1Ggpr7zivP3WrV4XX7MvB88ddN5OhbATKZUyWCIt/H14GV0T8x225nJxxiSMkQXI3FD+7qXs+Ppxtn40kv0LXqewII8dXz9B9qkDAJzbs4btX45n+1cTOLdnjc5pK0dKpQyWKAvpuemEx65n5Uo4af+1xooV6BtIWGCYTDvpZrwaWQjtfCfBTTsTceOjHP1rJtaCvIvP55w6QNOhrxHVdyIn187TMWnlSamUoX299viafVm6dymPPgqffuq8fcu0k+4v/Nr78KkRevFx3fiBKGYv0vdvIKBBax2TVZ6UShn8vPxYOmIp98c+wIwZ0K+fc/cdXSeatUfWOm+nQne5545zZuti6sYP0DtKpUiplEP7eh147aVA+vSB5s2du++72tzFlxu+dO5OhW7ys9PZP/9VIm58FMVkzGW5pFTKYd486NYN2rd3/r69TF50jejKn3v/dP7OhVMo/LOA3cGFMyjIzeTIsi84uvwrHVNVnmKUyYFSUlKsYec36rLvLTtrsmFbHYbevKPc7znp14Q62Tvtsn+r1cqUnV8ztckdKIrjVlC0Z2ZnMFpeMGbmI9VbExcXV+6/eIY6vgpPHKnTfmHVC5AR2pVmzcr3nrTUVMLbd7NbhtsbRJCSl0k/1XEndeyd2dGMlheMmflISsVGlMvHn3L6v/+DV6enk5Wlz5Fdz0Y9WbxnsUzkJFyelEo5JR9Zzid1azD22VTy8/XJcGfrO/lqozE/ZwvPIaVSTq1DWqOg0KDrUiZMAM2xa40VKz48ns3HN5OVl+X8nQtRTlIq5RToG0hsWCzbc5N48034/ntY6vilga4yKn4UH6Z86PwdC1FOUioVkBiZyNK9SzGbrYwaBUlJzs/QpFYTTmWd4kyWEyd5EaICpFQqwBJp4UjGEX5P2clzz8H48frkeKj9QzI1gnBZUioV0DWiK3V86jH7lwO88YZtVjg9XFP9GrxN3hxIO6BPACFKIaVSAea8mjxw/gAzp1zntFn2SzImYQzvrZGpEYTrkVKpgN27Ib78AwsdKtA3kIjgCLYc36J3FCEuI6VSAWfOQM2aeqf4x73t7uXTdU6ci0GIcpBSqYC0NAgO1jvFP7zN3nSo34G/9v2ldxQhLpJSqYCsLKhWTe8UlxscM5j/bv0vRrkxVLg/KZUKyM4GPz84kHaAz9d/rnccwLaY/IDmA5irOXECXSFKIaVSAZmZ4O8PKw6sYOTckRw6d0jvSAB0b9idpH1J5BfqdFOSEJeQUqmAEyegTh3bZNgAS/fpME6/BHe3uZuZ62fqHUMIKZWKKCwEkwlCA0JpVrsZS/e6Tqm0CW3DjtM7yMzL1DuK8HBSKhXQoAGsWmX7tSXSQtJ+HW7+KYWswyxcgZRKBdx3H/zvf7Bnj+3mwm0nt3Es45jesS6KqhFFek66rMMsdCWlUgGKAi+8AK+/Dm2Dr+Opbk9hxbUu5Y5tP5Z3kt/RO4bwYFIqFeTjAy++CF9/GM4L171AaEBo2W9yojr+dQjwCWDv2b16RxEeSkqlEoKCwGzWO0XJRieMlnMrQjdSKpXk7Q15eWW/Tg/+3v40rdWUDUc36B1FeCAplUqqXRtOufD50Lvb3s3MDTJuRTiflEolRUfD+vV6pyiZl8mLxMhEFu9ZrHcU4WGkVCrJYoGFC2H2ptncNecuveMUq7/an3naPAqthXpHER5ESqWSTCZo0wY2aeeZtXEWadlpeke6iqIo3N7ydr7Z/I3eUYQHkVKpgqFD4eCyRKxYWbZ/md5xitWxfkfWHVlHdn623lGEh5BSqQKzGaY/24xqBSEs3u1aQ/YvJWsFCWeSUqmiGjUUejSx8PUK17m58EpNajXhdNZpTmed1juK8ABSKnZwQ/NEjpnXcPR0ht5RSvRQ+4eYvmq63jGEB5BSsYPbW97OAzkaITWr6x2lRHX86xDkG8TuM7v1jiLcnJSKHQR516auV2MUxTWW7yiJTI0gnEFKxQ727IEmTfROUbZq3tVoeU1LVh9arXcU4cakVOwgKgr279c7RfkMazWMWZtmyez7wmFVeX+WAAAgAElEQVSkVOzAx8d1by68ktlkpk/TPvy8/We9owg3JaViJ4oChdZCTmae1DtKmXo17sXiPYtl9n3hEHZfZlxV1fbAtKKHw4EPgHPA/YAfMLOkx5qmnbN3HmcpLIQ7/ncHx88fZ8ndS/SOU6aR7Uby+frPuS/2Pr2jCDdj91IBmgI3ANcD64Fu2MpjOBAJPAH4lvDYsPMgms0QUyOeedpkcvJz9I5TptYhrfl609dk5GYQ4BOgdxzhRuz+8UfTtFmapmUD8UCKpmlbgE1AQyBU07TNpTw2rDFj4PAKC9n52aw+bIyrK2Pbj2VG8gy9Ywg345BzKqqqNgXqA3uLvmQGAoDsUh677sixcqhbFxr7x1LdK8Cl1gMqTf2g+hRYCziSfkTvKMKNKPa+tKiqajVgNjASGAH8he3jTXcgA1hW0mNN014sabspKSlWf39/u2a1t6wshX7fjSEyKod3OryDn5+f3pHKlJ6bzqfap4xvNZ7s7GxDZL7AaHnBmJkzMzOJi4sr98hOR5xTmQjUxXay9i9gArajkgeAHGBGKY9LFR0d7YC49tV2wc3kVluIr6+vIfICrMldA3XA76SfYTIDpKamGiovGDNzSkpKhV5v91LRNG0yMPmSL315xUuunCbNNadNq6QHWj1KePijKEqq3lHK7b7Y+5i4cCL3RciVIFF1Mk7Fzvz8INtg8yH5mH3oWL8ja06s0TuK29tfbT9z0ufwVdpXLD6/mAJrAXPS53Cm4MzF12QXZjM3Y65hRz1LqdhZaCgcPKh3ioobHDOY3w/+bti/yEYRkRVBgl8CDX0acl3161iVvYpCLp9DOCkriU5+nVz+BtWSSKnYWatWsHGj3ikqTlEUetfvzfdbv9c7ikfpXK0zgabAi4/35u1lX94+VmevZlPOJh2TVZ6Uip0pimuvXliauLpxJB9KNsTgPXe1JWcLN1a/kT4BfdByNXKsxvuzkFJxAJMJlh5eapjxKpe6L/Y+Pl77sd4xPJa34k1Nc03br/FGwXgfgaRUHCA3Fz7e9jFv/P2G3lEqrHmd5hzNOMrZ7LN6R/EYlxZHO992LM1cyoKMBYR6heKj+OiYrHLsPvjNUVJSUqx5McaYX2DTqgA++v0jtODPWfDwAkyKMbrbvNtMQaMCTp8/zbyN8xjRaYTekUp1Ia+RGDGz91Zv3Qe/OUzHah31jlAuHa+F1M0dWH3qbQLTA2kV0krvSOWSakolulo0VIPNvpsJzw0nIjhC71glupjXQIyYOYWKDX4zxn+hBjTMomJWvJg603jnVQDGJIzhvdXv6R1DGJCUioP4e/mTUC+eHblJHDumd5qKC/AJoEmtJmw4ukHvKMJgpFQc6LFOjzG+5xA+NOjigHe3vZvP13+udwxhMFIqDjQ4ZjD3dByMvz/s2qV3morzMnnRo1EPft/1u95RhIFIqTjBQw/BOwad065P0z7M3zGfgkJjXbEQ+pFScQJfX9soW4Ncvb+MoigMazWM2Ztm6x1FGISUihPMnAldutiG8BtRQr0ENh/fTFZelt5RhAFIqTjYN9+AtzcMHKh3kqoZFT+KD1I+0DuGMAApFQc6dw62pBbwvXkg32z+Ru84VdKoZiPSstMMsa6R0JeUigPl5kKNIDO7z+xmwc4FesepsofaPySz74sySak4UJ06cOYMxNW2GPKO5SvV9q9NDb8a7Dy9U+8owoVJqTjYU0/B4ZUW9qXtY9/ZfXrHqbIH4x/k/TXv6x1DuDApFQerVg3emNANgCV7knROU3W+Xr50qt+JJXtcf2lXoQ8pFSeIjriGxkHRvPVjkiHHqlzpluhbmKvNlQFxolhSKk7y290/MantdBYt0jtJ1SmKwvA2w/liwxd6RxEuSErFSRrXakz/Pn78/bfeSewjNiyWHad3kJ6TrncU4WKkVJxo61Yw2OJ0pXqo/UNMT56udwzhYqRUnOivv6BrV71T2E94YDg+Zh/2nt2rdxThQqRUnKh6dUhL0zuFfY1NGMu7q9/VO4ZwIVIqTnTrrfDu5yfJzMvUO4rdVPOuRmxYLMv3L9c7inARpU58rapqNDAMyAEUwAx8oWmaAacc0l8GR3jLN5yQ+d8zsf9gvePYzW0tbmPCrxPo1KCTYVYOEI5T4t8AVVUHAzcAkzVNe17TtOeA54Ehqqpe76yA7iQsMIyoGlF8szKJAjca4qEoCne0ukPmXBFA6Ucqv2ialgWgqmpHoFnR1w8Bxh8aqpPEyERWFizlyy9hxAi909hPx/odmZM6h/O556nuU13vOEJHJR6pXCiUIk9e8Vy2wxK5OUukhZ3pm1ibelrvKHY3tv1YZqyWu5g9XXk/ANcGxgAjgREOS+MBEiMTsWLluN8yvaPYXURwBIXWQg6eO6h3FKGj8pZKKnA3tkIZ6bA0HqBxzcZ0adCFPNxzasaH2j/EO8kGneVb2EV5S8Ub+D/gWWCKw9J4AEVRWHbPMiy1b2P7dr3T2F+ATwAxdWNIPpSsdxShk9Ku/tylqmonAE3TRmqado+maSOBWaqqDnBaQjc1ejS8/Tbk5+udxP7ubH0nszbOwuoOt2SLCivx6o+maV+qqjpSVdWbsV3xUYBwYJOmacaecNUFeHvDmDHw0Ue2gnEnJsXE4JjBfL/1e4a0GKJ3HOFkpQ5+0zTtMwBVVWsVPXa/SxY6iomBr7/WO4VjdIvsxhO/P0HfZn2p5l1N7zjCicp1TkXTtNNSKI5hcuMBqGMSxsh9QR6o1COVC1RVbQJ0pKiENE2T2Xmq6Ej6EWZtmkWeMhpwz8FiDWs2JKcgh0PnDlEvqJ7ecYSTlPf/yY+x/c1Xin6IKjqZeZIn/niC3bkr9Y7iUOM6jOPtVW/rHUM4UbmOVIDlwEeaphU6MownaXFNC6ortQhouRToqXcchwnwCaB1SGtWHFhB5wad9Y4jnKDUIxVVVZeoqroY6A/sVVV1cdFjUUVHj5hoYO3G9hzjrwdUlqGthvLN5m8otMr/SZ6grKs/3Z0VxNP88QfcEmvh1Q1PkpWX5dZXSC5MlD1z/UxGtpMB2e6uXOdUVFW97ZJfy+l8O4iJgaAzFnILcll1aJXecRwuPjyeHad3kJbtZlPfiauUWSqqqlqAvqqqJqqqeh3QyPGx3F9CAhzb2IZ/X/dvIoMj9Y7jFOM7jOetVW/pHUM4WHlO1F4LRALdgQLgaUcG8iStWpjpEjKRhjX1TuIcIQEh1PSrybaT22hep7necYSDlHmkomnaVGCGpmlTNU17QdO0NU7I5RHi42H9er1TONeo+FF8sOYDuS/IjZV3nEqKqqqvqqo6TVVV+fhjJ82bQ2qq3imcy8fsw/WNr+eXHb/oHUU4SHlL5XlgFvADICOZ7MTbGzIzcYv1lSvixqY3smj3InILcvWOIhygvKVyRNO0dUAKINN62VFiIixYoHcK57vwMUi4n/KWSndVVVOA1UCCDICznz594Ku/f6Xl2wnkF7rh5ColaF6nOaeyTnHi/Am9owg7K2+pJABDgb5AR03TrnNcJM8zamQAW86sYXHqWr2jONX4DuPlviA3VN5S+Rx4DmgBfOKwNB6qY4ME/Mx+vPZfz1r5pGa1mtQPqs/GYxv1jiLsqLylchbYpWnar44M46l8vXzp2KAjuwvd/z6gK90bey+frP1ELjG7kfKWyimgp6qqHwPHHZjHY1kiLRwy/8XhI260dGE5eJm8uLnZzczV5uodRdhJWXcpt1FVdQHQBTgHRGCbW0XYmSXSQpY1jdl/eNjAFaBX414k7UsiJz9H7ygO9/XOr7n+y+tp9V4r7p93P0P/N5SbZ9/Mbf+9jUJrIccyjtH7q94M+X4IqSeM+XehrCOV54C7NU3roWlaT2wna593fCzP07lBZw5MOETG7pZ6R9HF6PjRvL/mfb1jONwdTe7gqW5P0bdZX57o8gTDWg3j56E/U927OltPbGXKn1N4qcdLfDnwS15b+ZrecSulrHt/CgA/VVUjLvmaG8+qqh9fL1/qB4d73EC4C5rWbkra5jSOZRwjJCBE7zhO0ax2M5rVbkZeQR57zu6hWe1mHDx3kNiwWAD8vPx0Tlg5ZZVKGjD1iq+dc1AWj5eZCf7+eqfQz7gO43hl+Su82ONFvaM41TNLnmFChwn4mH3w9/7nL8CZ7DM6pqq8siZpkhl1nMhsds/Fxcqrhl8NGtZsyPqj62kb2lbvOE7x2brP8DZ50795fwDahLRhxYEVtLympWHPMclHGRfi6ws5xvx7ZDcj247ks3WfecQl5t1ndjP+1/EczTjKAz89wObjmxnbfizvrXmP4XOGM7HrRL0jVopilD+8lJQU60/pP+kdo9y6+Xfjr8y/Kvy+RZ91pd3t86hVrZYDUpWuspntbc+ZPWTlZxFTN6bU17lK3oowYua+gX2Ji4sr/yoaVqvVED/WrFljNZKtW7dW6n1dpjxpDXklxFpYWGjnRGWrbGZHePTXR62ZuZmlvsaV8paXETMX/dsr979V+fjjYtrV7sqx88fYfmq73lF09VD7h5ixeobeMUQlSKm4mCbeXTEpJpbu87wh+5dqWLMhBYUF7Du7T+8oooKkVFzMsf3BtA1tS9I+z7q5sDgPd3iYd5Lf0TuGqCApFReSnw9eXpAYkcjSfUs94gpIafy9/elQvwOL98j0PUYipeJCNm6Etm3h+sbX06JuC87nndc7ku4GRQ9i7ra5HjWBldFJqbiQJUugWzfbHK6/3vkrAT4BekfSnaIo3B93Px+mfKh3FFFOUiouwmqFEyegbl29k7ielte05FjGMZl60iCkVFzEhg22jz6ieBM6TuDNv9/UO4YoBykVF1GnDuzdq3cK11WzWk0a12rMmsOylp2rk1JxEfXrQ9Om8OabUFiodxrXdHebu/liwxcUWuUb5MqkVFzIoEG2E7Xjx8PmzbBgxwK+2viV3rFchtlk5o6WdzBr4yy9o4hSlGeBduFEcXHQujV89hm8d2Aup4OXcGfrO/WO5TI6NejEXG0u53JkWh9XJUcqLsjbGx54ACYMTGT/+e089OQRpk2zjWPx8PFwgG29oLf+fkvvGKIEcqTiwno2sQDQ7c4kete/jV9/hf/+11YstWpBp04QGws+PjoHdbKwwDCC/YLZfW430UTrHUdcQUrFhdULqkfjmo1J2pfEbS1v4/bb/3nu9GlYsQJefhlyc21HN+3a2YqmTh39MjvLg/EPct+393FT+5tQlPJP9SEcT0rFxSVGJhZ7x3KtWnDzzbYfYCuW9evhq6/g1Cnb0UzXrtCrl22aSnfjY/ahW1g3ftr+E/3UfnrHEZeQUnFxD8Q9wA1NbsBqtZb6P7KPD7Rvb/sBtsvSSUm2K0mTJkF4uJMCO1HX0K58vPdjrm98vWFnnndHcqLWxXWs35EhLYZU+BDfZIJrr4WYGDh40DHZXMHY9mOZkSyTObkSKRU3tWULPP44RET8c/TijhrVbEReYR4H0g7oHUUUsfvHH1VVg4G3AB/gfWAitrWC7gf8gJklPdY0TQYfVMHRozB/PmgaNGsG//63Z1wZerj9w0xdOpVpvabpHUXgmHMqCcAzQDCwEWiFrTyGA5HAE4BvCY9lmq8KOHoUVq603YyYmwuhoXDDDXDPPXonc67qPtWJD49n6d6lWKIsesfxeHYvFU3TFqqqGgk8CczRNG2Lqqo+2NZhvkbTtM2lPBYlsFph1y5YvBgOFB3ph4RA587Qt69txjhPdmvMrUz4dQJdIrrgZfLwb4bOHPXdPwVsAS78n2kGAoDsUh5XL2ujqamp9k3pQNnZ2XbJq2m+/PZbEIuCxlK/ej0mdR5Nt26Xz4K2Y0eVdwPYL7OzXJm3e43uPP/L89ze5PZS3qUvo32PK8MR51QGAX8CXwPPqqoai+3jz34gQ1XVOGwfd658XOaZtuho44yeTE1NrXLev/+Gbdtsdy6P+NnErtPL6NnzdTslvJo9MjvTlXmjiWbdknXUjaxLHX/XHAFotO8xQEpKSoVe74gjlW3AF0AWEA/8C9tRyQNADjCjlMfiEo0a2QazZWfbJsP+fsv3pOekE+gbqHc0lzW+43heX/k6L1z3gt5RPJYjzqlsAfpc8qW7rnhJWY9FkWuugalT4amnoM8ICwXWApYfWM4NTW7QO5rLqlWtFlE1olh7ZC2xYbF6x/FIMk7FxdWuDW+8AT9/Hk1d/7os3evZi4yVx8i2I/l8/ecev8SJXqRUDMDbGx59RCEsN5Hkw8l6x3F5ZpOZ21rcxtebv9Y7ikeSUjGIxo2hj/V9Prn2N72jGEKXiC5sPLaR9Jx0vaN4HCkVA3nqkTpMf0vGYJTXuA7jeHvV23rH8DhSKgZSvbrtbuMzZ/ROYgzhgeEE+ASw/dR2vaN4FCkVg6lXzzY8X5TP6ITRvLf6PTlp60RSKgYTHAxpaXqnMA4fsw+9m/Tm5+0/6x3FY0ipGMy5c3Cm4CDHMo7pHcUwbmhyA3/u/ZPs/OyyXyyqTErFQNatg7XrC7gtKUYWLK8gmczJeaRUDGLePPjtN3jpRTNdIroUO2+tKFmjmo3IL8xnf9p+vaO4PSkVA/jgA9t5lCeftE0TaYm0sOLACnILcvWOZigPd3iY6aum6x3D7UmpuDCrFf7zH2jYEO665A4pS6SFrPwsUg5X7O5RT+fv7U/H+h1ZtHuR3lHcmpSKiyoshClTbJNXX3/95c/Fhcfh7+1P0r4kXbIZ2S3Rt/DT9p/IK8jTO4rbklJxUW+8AbfeCh07Xv2cj9mHu1rfRbBfsPODGZyiKIyKG8UHKR/oHcVtSam4qKio0md0e//m93kw/kGn5XEn0XWjOZ11mqMZMorQEaRUXNSgQZCeDp99pncS9zSh4wRZ5N1BpFRc2N132yZqmjbNdtJW2E+QbxAtrmnBigMr9I7idqRUXFyfPmCx2GZ/y88v+/Wi/Ia1GsY3m7+hoLBA7yhuRUrFADp0gJEj4ZFH4PhxvdO4D0VRGNl2JJ+tl8+Y9iSlYhBNm8JLL9lWHdyyxfa1s9lneX7p8+w4Zac1OjxQu7B27Du7j1OZp/SO4jakVAwkIABeew0++ggyMmyXlp9Pep4FOxfoHc3QJnScwFur5KStvUipGIzZDP37Q3KybYRoQr0EuQ+oimr716ZBUAPWHVmndxS3IKViQA0awKFDtl9bIi0k7UuSSYiq6J529/DZ+s/k+2gHMuGpAYWGwpEjtl9bIi28tOwlUk+mElM3Rt9gBmY2mbm95e3M3jSbYa2HOWw/c+duYerUPzl+PJ327RsSEVGb3btPoCjw8su3curUecaNm0VQkB+PP34jTZuGOCyLo8iRigEFBMD587Zfd27QGbNilvWA7KBzg85sOr6JcznnHLaP/v1bMH58L3r1asHkyf2oVas6r756G9Wr+7Jt21FefXUBEyf24e237+T995c4LIcjSakYVEaGbRa4QN9APuv/GT0b9dQ7kltw5khbf39fbr+9A99+m8yRI2moaihHjqTRunUDfH298PX1dkoOe5NSMahJk+DppyEpCe5qcxdNazfVO5JbCA0IpYZfDbad3Oa0fbZqVZ+srFyOHTtHtWr/FElaWqbTMtiTlIpB1a4Nb71lGwz36KOwcqXeidzHg/EP8v6a9x1+0vbgwTP89NN6YmLCadOmAQcOnCYmph6rV+8hPT2b3FxjDqGWE7UGpigweDAMHAjffQc//ABjx9rucBaV5232pp/ajx9Sf2BQzCCH7Sc8PJhp07bw66+b8Pf3ISEhiubNw3j66f+RkZHDI49cX/ZGXJBilEtoKSkp1iVLTugdo9zi4mqQknLWqfvMyzOxfHkEAF277sPLq2J/tnpkrgpH5/059xN6et+Bn+Jvt20a7XsM0L17XeLi4pRyv8FqtRrix5o1a6xGsnXrVt32vXOn1TphgtU6f77VWlhY/vfpmbkyHJ338LnD1uf+fM6u2zTa99hqtVqL/u2V+9+qnFNxEymHU2j0ViP2nNlD48bw+uu26RIeeQT+/lvvdMYUFhhGbf/abD6+We8ohiKl4iYia0Sy5+yei/PWKgrcdJOtXHbvhsce+2fAnCi/B+Ie4KOUj2SkbQVIqbiJOv51aFG3xVX3AZlMMHQoPP+87UemMa9S6sbL5MUdre7gy41f6h3FMKRU3EhiZGKJM+z7+9vGtTzxBBw+7ORgBtexfke2n9rO6azTekcxBCkVN2KJtLDrzC4OnTtU7PPh4bZ1hGbNgokTbcuoivJ5pOMjvLHyDb1jGIKUihuxRFlQUFh7ZG2JrwkMtB2tTJ1qm+zpscfgp5+cGNKgavvXpnGtxiQfStY7isuTUnEjoQGhnPrXKfqqfct8rY8P3HknvPoqFBTA1187IaDBDW8znFkbZ8mctmWQUnEzNavVrNDrFQWaN4c8WbCvTCbFxL2x9/Lx2o/1juLSpFQE8+ZB37IPbgTQOqQ1x84fk4XISiGlIjh/HmpW7ADHo03oOIE3/35T7xguS0pFkJOjdwJjCfINIjYslj/3/ql3FJckpeKGsvOzWXVwVble+/XX0KqVgwO5oVtjbuXHbT+SW5CrdxSXI6Xihr7Z/A0dP+lY5lo2335ruz9omOOmZHVbiqIwJmEM765+V+8oLkdKxQ0lRiYC8Nf+v0p93fr1tiH8onKa1W5GTn4O+87u0zuKS5FScUMNazSkflD9MifDjoiAzXIDbpWM6zCOt1e9rXcMlyKl4oYURcESaSlzkbH774ePZchFlVTzrkaPRj34efvPekdxGVIqbsoSaWH90fWczS55ljEvL+jaVea3raqbmt7Ekj1LyMyTW8BBSsVtdW/Ynf7N+5daKgADBsBXX8nHoKoa33G8fAwqIqXipprUasKc2+YQVSOq1Nd5ecGbb8Ly5fDaa3VZtMh2RUhUTERwBNW8qqGd1PSOojspFYG3N4waBY88coK8PHjySZgxA9LS9E5mLKMTRvPemvc8fpY4KRVxkckEN9wAL78MffrYjmCmT5cjl/LyMfswsPlAvtvynd5RdCWlIooVFQVTpkBCgq1YRPlYoiysPbLWoesxuzopFVGqjh1tazafMM6SS7p7pJNnzxInpeLmFu1exAtJL1RpG/feC1/KvM/lFhoQSkhACOuPrtc7ii6kVNzcpuObeG7pc1UaQxEWBmfO2DGUB7g/9n4+XfcphdZCvaM4nZSKm7NEWsgrzCv3XcvFycuzncQV5Wc2mRneZjifrftM7yhOJ39V3FzrkNYE+waXOWS/NElJ0K2bHUN5iPjwePal7eP4+eN6R3EqKRU3ZzaZ6RbZrUqlsmaN7SqQqLjHOj3mcSdtpVQ8gCXSwt8H/yYnv3JTvA0aBK+9JuNVKiPYL5g2oW08apY4KRUPMDhmMN8O/hZFUSr1/iZNoHdveO45KZbKuK3FbcxJneMxs8RJqXiAqBpR9FP74WP2qfQ2unSBm2+GZ56RYqkoRVF4qP1DvJP8jt5RnEJKRZRbXJzthO0vv+idxHia1m5KfmE+BzIO6B3F4aRURIVcfz0sXAj5+XonMZ5xHcbx1Y6v3P6GQykVUSGKYpsx7h3POJK3Kz8vPyxhFn5I/UHvKA4lpeJh7PG/ZIsWUFgIqal2CORhOod2ZtWhVaTnpOsdxWGkVDzII78+wqDvBtllWz17QkqKXTblcR7t9Civr3xd7xgOI6XiQWpWq8miPYsoKCyo0nYyM+H992HwYDsF8zAXbjhcd2Sd3lEcQkrFg1giLZzLOVelu2etVpg61XZp2c/PjuE8jDvfcCil4kE61O+Aj9mHpH1Jld5GUhJ06GC7c1lUntlkZkTbEXy67lO9o9idlIoH8fPyo0O9DlW6D2jRItsM/KLq4sLjOJB2gGMZx/SOYldSKh7GEmkh9WRqpa8C+fjAqdKXaBYV4I4nbaVUPMykbpNIHZta6fuAxo2DF16ApZU/2BGXCPYLJi48jsV7FusdxW6kVDxMNe9qmJTK/7EHBdlm2T982LaUx+nTdgznoW6NuZV52rxK30XuaqRURIUpCtxxB/zrX7ajlo0b9U5kbBduOJye7B7LFkipiEqrVQtefdW2yLvcC1Q1TWo1wWq1svP0Tr2jVJmUiqgSkwmGDIH58/VOYnzjOozjneR3DH/DoZSKh9pxagfbTm6zy7a6dIFly+yyKY/m6+VLP7Uf/936X72jVImUioe643938NzS5+yyLUWBzp1h3jy7bM6jXdfwOlYfXm3oFQ6lVDyUJdLC0n1L7XaoPWAArF8PmmaXzXm0xzo9xmsrXtM7RqVJqXgoS5SFw+mH2XVml922OWkSvPsuHHOvAaJOFxIQQv2g+qw9slbvKJUipeKhukZ0RUGp0n1AV/Lygn//23aZeafxL2Lo6t7Ye/ls3WdVvqNcD16O2rCqqt8DrwBTgXPA/YAfMLOkx5qmGfeDpMHUqlaLViGtWLpvKfe0u8du261eHV5/3bakR/Xq8OCD4O1tt817DJNi4p529/Dx2o8ZFT9K7zgV4pBSUVW1J5AAPA48AfgCw4HIMh7LJIVONLLtSIcsG+HtbRttu3UrPPYY3HkntG9v9924vXZh7ZirzeVoxlFCA0L1jlNudv/4o6qqFzAC2xFIjqZpm4FNQEMgtIzHwokmdJzAv7r8y2Hbj4mxDenftAmefx5yPWPZG7t6rNNjhrvh0BFHKg8BbwM3Ahdm3TADAUB2KY+rl7XhVANNipqdnW2ovOC4zJ07w4ED3owcWYsxY05Sq5Z9zhN4yvc43BrOJ39+QueQzg5KZV+OKJW2QATQCYhXVTUW27mT/UCGqqpx2D7uXPm4zAVRoqOjHRDXMVJTUw2VFxybOToaOnWCSZNq8X//B/XrV32bnvI9bt68OY/89gjDmg7Dz8v50+2lVHAyYrt//NE0bYSmaY8CC4BY4BFs500+BD4DJpTyWLixwEB45RV4+WW5u7kiFEVhXIdxvL3qbYUj3GEAAA1oSURBVL2jlIvDrv5omnZhuOZdVzxV1mPhxvz8bGsyv/iirWAqOa2Lx2lUsxFmxcyOUztoWrup3nFKJeNUPFxBYQGTFk1i0e5FTttnzZq2Bd+//dZpu3QLF9ZjdvUbDqVUPJzZZOa/W//r9JvYevWyLUZ2wP2XFrYbXy9fbom+hW82f6N3lFJJqQgSIxOrNBl2ZT35JLxtjNMELsMSZWHjsY2cznLdk1JSKuLiZNinsp07o3W1arJ2UGU83vlxl77hUEpFYImyALDmxBqn71tO1FZcbf/aNK/TnOX7l+sdpVhSKoKI4AiiakTpUirnz0OB8e6Z092dre/km83fOOQ2i6qSUhEAfNz3Y4Y1Heb0/Q4dCl984fTdGt6FybJnJM/QO8pVpFQEAD0a9SAqMMrp+42Lk2kSKkuto5JXmMfuM7v1jnIZKRWhq40boVkzvVMY17gO43jr77dcauyKlIrQ1Vdf2T4Cicrx8/JjQPMBfLflO72jXCSlInTl7S2TOFVV94bdWXd0HWezz+odBZBSEVewWq0UWgudtj9Fkas/9vBYp8d4dcWrescApFTEJU5knSD89XB+2/mb0/Z5zTVw9KjTdue26lavS5NaTVh5YKXeUaRUxD/q+NWh0FrotCH7R47AoUNQr55Tduf27m5zN7M3zSavIE/XHFIq4iJFUZx2H1BOjm3W/SlTHL4rj6EoCmMSxjBjtb5jV6RUxGUskRbWHF7D+dzzDt3P9OnwxBPg7+/Q3Xic6LrRZOdns+fMHt0ySKmIy1giLeQX5rPyoOM+mxcWwsmTEBXlsF14tAkdJ/DWKv3GrkipiMu0uKYF11S/hh2ndjhsH8nJtvlqhWP4eflxc7ObdVvoXUpFXMakmDjwyAFGJ4x22D4yMx22aVGkZ6OerD68mrTsNKfvW0pFXMXH7OPQ7XfvDnv2wBtvSME40uOdH+e1lc6fd8VhE18LURJFgQkTbMXy0ktgMsHgwdCypcyvYk/XVL+GqBpRrDq4ig71Ozhtv1IqQjcNG9pWLjx/HubMgdmzbVeDevWC+Hjbgu+iaka0HcH4BeOJC4/Dy+Scb6j8sQndVa9uW28ZbAWzaJGtbPLzISTE9nGpRQvbEY2oGJNiYnTCaN5b/R4Pd3jYKfuUUhHFOp97nqR9SfRo1MPh51guVb069Otn+wFw7BgsWQLffQdWq21lw+7dZbqEioipG8OP235kf9p+IoIjHL4/KRVRrM3HN3PT7JtYNnIZXSK66JYjJARuv/2fxwcO2Ermyy/hxIm6dO4MAwdCUJBuEQ1hQscJPL34aV7v7fjF3qVURLFiw2Kp7l2dpfuW6loqV2rQAIYPt/1669YTmEx1mD4dMjJs5dOmjb75XJW/tz89G/VknjaPfmo/h+5LPqWKYnmbvekS0YWkfUl6RymRokDz5vDUU7alVNetg8cft80mJ652U9ObSNqX5PBbMKRURIkskRaWH1hOfmG+3lHK5O0NI0bAf/4Df/8NkybB4cN6p3I9EzpO4I2/33DoPqRURIkSIxPJyM1g7ZG1ekcpNy8veOABmDjRNkv/yy9DVpbeqVxH/aD61PSryebjmx22DykVUaKE8ASGtRrm1Ks/9hIYaFtW9Y47bB+JDh7UO5HrGBU/ig9TPnTYDH9SKqJEvl6+fHXLV7QNbat3lEqLiIDXXoO33oLlrrmgn9N5mbwY1moYX2xwzIJLUirC7fn5wbRpsGuX7ajll18gT9/J0XTXoX4Hdp7eycnMk3bftpSK8AiKYrsUPW2abUzLc8/BM8/An3967sTbj3V6jNdX2n/cioxTER7FZIJu3Ww/8vJstwRMngxmM/TubZvnxVNuB6hZrSYt6rbgr31/0S2ym9226yHfPiGu5u0NN9wAL75oG+ty6tQ/V41caME/hxraaijfb/3ergu9S6mIMv198G9G/DjCqesBOZuvr+1+o5dfhtBQ+OgjvRM5h6IojE0Yy7ur37XbNqVURJnO5Zxj5oaZpJ5I1TuKU1x/ve0StKdMIKXWUcnOz2bv2b122Z6UiihT5wadMStmp60H5Arat/es4f7jO4xn+qrpdtmWlIooU4BPAHHhcR5TKunpMH8+tGqldxLnqeZdje4Nu/PL9l+qvC0pFVEulkgLS/cu1W3ZB2c5ehT+7/9g6lTb3C6e5OZmN7NozyKy8qp2X4OUiigXS6SFY+ePsf3Udr2jONQHH8Crr0Ldunon0ce4DuOYnly1j0FSKqJcEiMT+f3O350yc5iennnGs1dNjKoRhb+3P+dyzlV6GzL4TZRLoG8gvRr30juGw3nKwLfSPNT+oSq9X76FQgi7klIRQtiVlIoQwq6kVMT/t3d/MXKVZRzHvwuVUmsJNPiHaDSVwE8sYu1iKf4jTVSQgNxICKWUKkg0FHRNITZViqKgEP8DUkDB8i9QLuBGrFyUFL2wyaamtpAn1qTBqiRoFGigUOx48byTDmt3C+M77Dnr75Nsds7M9J0n0zm/ec+Z5501q8qhYmZVOVTMrCqHiplV5VAxs6ocKmZWlUPFzKpyqJhZVQ4VM6vKoWJmVTlUzKwqh4qZVeVQMbOqHCpmVpVDxcyqcqiYWVUOFTOryqFiZlU5VMysKoeKmVXlUDGzqhwqZlaVQ8XMqnKomFlVDhUzq8qhYmZVOVTMrCqHiplV5VAxs6ocKmZWlUPFzKpyqJhZVQ4VM6vKoWJmVTlUzKwqh4qZVTWt9oCSTgJWAzuAO4BvAM8CnwcOBX4x3nZEPFu7HjN7fVUPFWAecHlEbJN0PXA5MB1YCrzrANs3DKAeM3sdDeLw5wPAiKQHgXkRsRX4AzAHeNsBts2s5QYxU1kREbvKYdBvynUHA28Cdk+wPfNAA4+OjlYudbDaVi+0r+a21QvtrPm1GESoLAZuAWYDV0oaJg9vngR2TbD954kGHR4eHhpArWZW2SBCZXc59HkZGAGuIWclFwMvAjdOsG1mLTfU6XQmuwYzm0Lcp2JmVTlUzKyqQZxTqUrSW2hBg5ykBcB1ZXMpsIaG19wlaR1wPT2Nik2uWdLxwBLg+zT8tVE+Bf0S+enmpTT8dSHpfOBw4D76bFRtfKiQzXFtaJA7BjgN+CTwe+Cj5H9Ek2tG0seBDwIraMfzDHAVcCHwNZpf83nAJcAJwGbgYzT0dSHpBOAUYAv/vd+96kbVNhz+tKJBLiLujojdwInAaERso+E1S5oGLCPfgV5sw/Ms6TPAe4HbgXe3oOb15A54GfCrJr8uImILsBYY4n9oVG1DqLymBrnJJOkY4B3kuidofs3LgR8De4GjynVNr3kx8ClySn5Eua7JNS8kDyUeAE4u1zW53m4/WN+Nqm04/Nn6ahvkJpOkGeQ5lc8Cy9pQM7lO653ki/1ESfPJqXmTa/4n8BfyuR1uQc3HA1cDhwE3taDerq39Nqq2IVRupx0NciuBN5PB8hjwZRpec0QsA5B0JVnnFTS8ZuBm4E5gFnAG2WDZ5JrvAB4lZ4Ofpvn1AnTI/e4m+mhUdfObmVXVhnMqZtYiDhUzq8qhYmZVOVTMrCqHiplV5VCx1pI0V9K8Pv/tYkkH167J2tGnYhOQtBr4MPAMsC0irprcivaR9BTw67J5XWnzrjX2dLKrdkTSXmAYWAScSfaGXEw2bT0TEV/YzxCbyLUs36lVkyXPVKaGb0XE2cB8SbMmu5gej0fE0vKz30CRdIGkC/oY+xzgpxHRAV4gF+wdSzZudYA1EXEusKesan6FiNgOTJN0aB+PbRPwTGVqmQ7cKimAoyNiiaT3kV2cM4G7gV8C9wB/B3ZFxBWSLiX/CsIRwEXkmqANEbFR0oaIWCRpA/AwcFREjEhaRK7AnQ18NyJ+t596jpP083J5ObksYCWwB/ghMAM4G+hImhERN0taTq6XmUYuwvsiuUL2SWB2RFxWxpsTEWvL5a3AXLJrtau7huUwssX8EnKF8HTgwoj4B7lq+Ljy2yrxTGVqWCnpLuA28l17Z0QsKbetAv5F7pQfItedvJ0MiDXlPu8nd6yrgV1jxu5tud4cESPl8mrgaWAn+xbKjfVERHyu/DwP/BX4Wfl9ZkSsB9YB60qgvBE4vdT+I3KpQwd4rBzWzR+nrufJ7wB5qWwPARdJWgtsiogdwChwPxlkC3vu55byyhwqU8O1EbEkItaV7fU9t3XILzP6OrlsHXLWMESuoYFc07EJ+CpwEvmO/wZJM4G3dgeKiEd6xv03+b0m1wDbx6lr7F9AWAUcCTxErnbtjtM7Y+7ONg5i3w7/p/J7T8/9/ihpbs/2LOAJ9gXFreWw68Zy+w+A54ANPY89H3h8nNqtTz78ab+x77Rjt68FflIu31JuX0HuYN3zHJ8AjibfxXcCG8ngWVjut79xvw3cS+6g3xyntveU2QLkIdd2YAHwkZ7xNgK3STqFPPG6XtKdwCHkN6X1nmTtreEB4HuSvlKu30KG5jnj1LIDOJWclf2tBNJzEfHSOPe3PnlBoVUhaTG5grU7U/ht+UKiQT7mHGBmP58qSTorIh4aQFn/9xwqZlaVz6mYWVUOFTOryqFiZlU5VMysKoeKmVXlUDGzqv4DbBV5altmBoMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129c8f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_pres, ax_pres = plt.subplots()\n",
    "ax_pres.invert_yaxis()\n",
    "\n",
    "well_cug1.get_log(\"Overburden_Pressure\").plot(ax_pres, 'g', label='Lithostatic')\n",
    "ax_pres.plot(well_cug1.hydrostatic, well_cug1.depth, 'g', linestyle='--', label=\"Hydrostatic\")\n",
    "pres_eaton_log.plot(ax_pres, color='blue', label='Pressure_Eaton')\n",
    "well_cug1.plot_horizons(ax_pres)\n",
    "\n",
    "# set figure and axis size\n",
    "ax_pres.set_aspect(2/50)\n",
    "ax_pres.legend()\n",
    "fig_pres.set_figheight(8)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
